This paper presented an entropy evaluation method for the influences of condense heat recovery system on the environment.Aiming at the damage of the condense heat to the environment,an entropy of resource loss and an ...This paper presented an entropy evaluation method for the influences of condense heat recovery system on the environment.Aiming at the damage of the condense heat to the environment,an entropy of resource loss and an emission entropy from the condense heat recovery system in the air conditioning refrigerating machine were introduced.For the evaluation of the entropies,we developed a new algorithm for the parameter identification,called the composite influence coefficient,based on the Least Squares Support Vector Machine method.By simulation,the numerical experiments shows that the Least Squares Support Vector Machine method is one of the powerful methods for the parameter identification to compute the damage entropy of the condense heat,with the largest training error being-0.025(the relative error being-3.56%),and the biggest test error being 0.015(the relative error being 2.5%).展开更多
The machine tool coolers are the best managers of coolant temperature in avoiding the deviation of spindle centerline for machine tools. However, the machine coolers are facing the compressed schedule to phase out the...The machine tool coolers are the best managers of coolant temperature in avoiding the deviation of spindle centerline for machine tools. However, the machine coolers are facing the compressed schedule to phase out the HCFC （hydro-chloro-floro-carbon） refrigerant and little attention has been paid to comparative study on sizing capillary tube for retrofitted HFC （hydro-floro-carbon） refrigerant. In this paper, the adiabatic flow in capillary tube is analyzed and modeled for retrofitting of HFC-407C refrigerant in a machine tool cooler system. A computer code including determining the length of sub-cooled flow region and the two phase region of capillary tube is developed. Comparative study of HCFC-22 and HFC-407C in a capillary tube is derived and conducted to simplify the traditional trial-and-error method of predicting the length of capillary tubes. Besides, experimental investigation is carried out by field tests to verify the simulation model and cooling performance of the machine tool cooler system. The results from the experiments reveal that the numerical model provides an effective approach to determine the performance data of capillary tube specific for retrofitting a HFC-407C machine tool cooler. The developed machine tool cooler system is not only directly compatible with new HFC-407C refrigerant, but can also perform a cost-effective temperature control specific for industrial machines.展开更多
Our company is a member of China Refrigerating Air Conditioner Industry Association, and Shanghai Refrigerating Air Conditioner Machinery Industry Association as well. In addition, it is also a member of China Washing...Our company is a member of China Refrigerating Air Conditioner Industry Association, and Shanghai Refrigerating Air Conditioner Machinery Industry Association as well. In addition, it is also a member of China Washing Machinery Garment Machinery.展开更多
Idealized cycles of refrigerating machines with adiabatic and isothermal compression of refrigerant vapor were investigated. Energetic characteristics of cycles： specific mass and volume cooling capacity q0 and qv, w...Idealized cycles of refrigerating machines with adiabatic and isothermal compression of refrigerant vapor were investigated. Energetic characteristics of cycles： specific mass and volume cooling capacity q0 and qv, work of compression 1, refrigerating coefficient of performance e and power N for drive of compressor were compared. These characteristics were calculated for eight refrigerants at temperature of their condensation 30 ℃ and temperatures of boiling -15℃ and -30 ℃. The calculations show that the use of isothermal compression of refrigerant vapor ensures economy of energy during refrigerating machine operation.展开更多
Data-mining techniques using machine learning are powerful and efficient for materials design, possessing great potential for discovering new materials with good characteristics. Here, this technique has been used on ...Data-mining techniques using machine learning are powerful and efficient for materials design, possessing great potential for discovering new materials with good characteristics. Here, this technique has been used on composition design for La（Fe,Si/Al）（13）-based materials, which are regarded as one of the most promising magnetic refrigerants in practice. Three prediction models are built by using a machine learning algorithm called gradient boosting regression tree（GBRT） to essentially find the correlation between the Curie temperature（TC）, maximum value of magnetic entropy change（（？SM）（max））,and chemical composition, all of which yield high accuracy in the prediction of TC and（？SM）（max）. The performance metric coefficient scores of determination（R^2） for the three models are 0.96, 0.87, and 0.91. These results suggest that all of the models are well-developed predictive models on the challenging issue of generalization ability for untrained data, which can not only provide us with suggestions for real experiments but also help us gain physical insights to find proper composition for further magnetic refrigeration applications.展开更多
基金Supported by Program of Science and Technology of Hunan Province(2007FJ2006)Project the Program of Science and Tech-nology of Hunan Province(2007TP4030)Hunan Provincial Natural Science Foundation of China(08JJ3093)
文摘This paper presented an entropy evaluation method for the influences of condense heat recovery system on the environment.Aiming at the damage of the condense heat to the environment,an entropy of resource loss and an emission entropy from the condense heat recovery system in the air conditioning refrigerating machine were introduced.For the evaluation of the entropies,we developed a new algorithm for the parameter identification,called the composite influence coefficient,based on the Least Squares Support Vector Machine method.By simulation,the numerical experiments shows that the Least Squares Support Vector Machine method is one of the powerful methods for the parameter identification to compute the damage entropy of the condense heat,with the largest training error being-0.025(the relative error being-3.56%),and the biggest test error being 0.015(the relative error being 2.5%).
基金supported by Science Council of Taiwan, China (Grant No. NSC 98-2622-E-167-029-CC3)Industrial Technology Research Institute of Taiwan, China
文摘The machine tool coolers are the best managers of coolant temperature in avoiding the deviation of spindle centerline for machine tools. However, the machine coolers are facing the compressed schedule to phase out the HCFC （hydro-chloro-floro-carbon） refrigerant and little attention has been paid to comparative study on sizing capillary tube for retrofitted HFC （hydro-floro-carbon） refrigerant. In this paper, the adiabatic flow in capillary tube is analyzed and modeled for retrofitting of HFC-407C refrigerant in a machine tool cooler system. A computer code including determining the length of sub-cooled flow region and the two phase region of capillary tube is developed. Comparative study of HCFC-22 and HFC-407C in a capillary tube is derived and conducted to simplify the traditional trial-and-error method of predicting the length of capillary tubes. Besides, experimental investigation is carried out by field tests to verify the simulation model and cooling performance of the machine tool cooler system. The results from the experiments reveal that the numerical model provides an effective approach to determine the performance data of capillary tube specific for retrofitting a HFC-407C machine tool cooler. The developed machine tool cooler system is not only directly compatible with new HFC-407C refrigerant, but can also perform a cost-effective temperature control specific for industrial machines.
文摘Our company is a member of China Refrigerating Air Conditioner Industry Association, and Shanghai Refrigerating Air Conditioner Machinery Industry Association as well. In addition, it is also a member of China Washing Machinery Garment Machinery.
文摘Idealized cycles of refrigerating machines with adiabatic and isothermal compression of refrigerant vapor were investigated. Energetic characteristics of cycles： specific mass and volume cooling capacity q0 and qv, work of compression 1, refrigerating coefficient of performance e and power N for drive of compressor were compared. These characteristics were calculated for eight refrigerants at temperature of their condensation 30 ℃ and temperatures of boiling -15℃ and -30 ℃. The calculations show that the use of isothermal compression of refrigerant vapor ensures economy of energy during refrigerating machine operation.
基金supported by the National Basic Research Program of China(Grant No.2014CB643702)the National Natural Science Foundation of China(Grant No.51590880)+1 种基金the Knowledge Innovation Project of the Chinese Academy of Sciences(Grant No.KJZD-EW-M05)the National Key Research and Development Program of China(Grant No.2016YFB0700903)
文摘Data-mining techniques using machine learning are powerful and efficient for materials design, possessing great potential for discovering new materials with good characteristics. Here, this technique has been used on composition design for La（Fe,Si/Al）（13）-based materials, which are regarded as one of the most promising magnetic refrigerants in practice. Three prediction models are built by using a machine learning algorithm called gradient boosting regression tree（GBRT） to essentially find the correlation between the Curie temperature（TC）, maximum value of magnetic entropy change（（？SM）（max））,and chemical composition, all of which yield high accuracy in the prediction of TC and（？SM）（max）. The performance metric coefficient scores of determination（R^2） for the three models are 0.96, 0.87, and 0.91. These results suggest that all of the models are well-developed predictive models on the challenging issue of generalization ability for untrained data, which can not only provide us with suggestions for real experiments but also help us gain physical insights to find proper composition for further magnetic refrigeration applications.
